Each year the CDC estimates that there are approximately two million cases of nosocomial infection that result in over 80,000 patient deaths. Antibiotic resistance is an evolutionary consequence of successful drug therapies and highlights the role of natural selection in shaping the molecular mechanisms leading to resistance. To examine these mechanisms in molecular detail, we are subjecting large populations of bacteria, carrying one of three antibiotic resistance genes, to continuous experimental evolution. By varying the conditions of selection during adaptation, antibiotic resistance mediated by changes to the target genes will be used to: 1) identify the network of mutations that define the functional intermediates to adaptation within the population; 2) determine the physicochemical basis for changes in protein function that lead to increased fitness (i.e. resistance) and 3) provide data for the successful modeling of successful evolutionary trajectories using correlated sign epistasis models. The target genes for study are E. faecalis Tn916 tetM (ribosomal protection against tetracyclines), Bacteroides Tn4400 tetX (enzymatic inactivation of tetracyclines) and TnA TEM-1 (enzymatic inactivation of (-lactams). These studies will provide a wide range of data and results including: high resolution crystallographic structures of Bacteroides Tn4400 TetX and E. faecalis Tn916 TetM, libraries of characterized expanded spectrum TetX and TetM mutants for drug development, a scalable high throughput TetM activity assay, and development of robust turbidostat systems for continuous evolution. By taking an interdisciplinary approach combining biophysical and population strategies, we can link changes in proteins at the atomic level to their consequences for the organism in its environment and vice versa. Developing validated models for molecular adaptation is an important step towards making accurate predictions of antibiotic resistance. Once fully realized, evolutionary forecasting holds the promise of going beyond the identification of traditional drug targets to the development of new clinical strategies that consider the molecular mechanisms of adaptation to prevent drug resistance. PUBLIC HEALTH RELEVANCE: The rise of antibiotic resistance is a clear health threat that requires immediate and continuing attention. As strains of drug resistant bacteria continue to spread into hospitals and communities the cost to society are staggering. Data from 2004 show that despite the best efforts of the medical community, methicillin resisistant Staphylococcus aureus (MRSA) were found in over 60% and vancomycin resistant Enterococci (VRE) in nearly 30% of ICU patients compared to 37% and 14% respectively in 1995 and continue to rise. Children and the elderly are particularly vulnerable. Unfortunately, community associated (CA) outbreaks of MRSA are also increasing suggesting that drug resistant strains are making their way into the locker rooms of schools and other public areas. In addition to human-to-human transmission within clinical settings, the widespread use of antibiotics in agriculture and aquaculture has also led to the proliferation of resistant strains. Mobile genetic elements such as transposons, conjugative transposons and plasmids act as vectors between microbial populations and can spread resistance beyond the agricultural or clinical environment. Hospitals and farms can therefore act as breeding grounds and reservoirs for the transfer of drug resistance genes into the general community. Although there is no way to stop evolution, a more complete understanding of the principles underlying molecular adaptation to resistance can be a powerful asset to both scientists and clinicians. The proposed work is an important step in the transition of molecular evolution from a retroactive posture that analyzes past events to one in which evolution research is used pro-actively for the prediction of drug resistance, optimization of drug regimens, and perhaps development of novel reagents that restrict pathogenic adaptation with direct application to medicine.